For texture modeling, direct regression of pixel values in the UV texture map often leads to blurry images [1]}. Therefore, the mainstream approach is to regress pixel coordinates, , learning texture flow from the input image to the texture map. Although texture flow is easier to regress and usually provides a vivid front view result, it often fails to generalize well to novel views or occluded regions.
Our approach directly predicts the texture pixel values by incorporating a pre-trained GAN. In contrast to the texture flow approach, it benefits from a holistic understanding of the objects given the generative prior and offers high plausibility and fidelity at the same time.
